stationarity-test

There are 12 repositories under stationarity-test topic.

  • MAKAHO

    super-lou/MAKAHO

    🥤 MAKAHO (for MAnn-Kendall Analysis of Hydrological Observations) is an interactive cartographic visualization system that allows to calculate trends present in data from hydrometric stations with flows which are little influenced by human actions

    Language:R8201

  • MoinDalvs/Forecasting_Airline_Passengers_Traffic

    Forecast the Airlines Passengers. Prepare a document for each model explaining how many dummy variables you have created and RMSE value for each model. Finally which model you will use for Forecasting.

    Language:Jupyter Notebook7101

  • JavadDogani/Multivariate-Cloud-workload-analysis

    This repository analyzes the Multivariate workload data of Google Cluster machines.

    Language:Python6102

  • super-lou/EXstat

    🌾 R package to provide an efficient and simple solution to aggregate and analyze the stationarity of time series

    Language:R4220

  • ssoudan/unit-root

    Unit root tests in Rust

    Language:Rust3231

  • super-lou/AEAG_toolbox

    🛠️ R toolbox to provide a simple way of interacting with all the code necessary to carry out hydrological stationnarity analysis for the Agence de l'Eau Adour-Garonne (AEAG)

    Language:R3100

  • Honey28Git/Time-Series-Forecasting

    Forecasting Wine Sales of Two Different types of Wine. After thorough Data Analysis, different models have been used and tested such as Exponential Smoothing Models, Regression, Naive Forecast, Simple Average, Moving Average. Stationarity of the data is checked. Automated Version of ARIMA/SARIMA Model built. Comparison of Models.

    Language:Jupyter Notebook110

  • karakastarik/stationarityR

    Automating time series stationarity tests

    Language:R0101

  • mbsuraj/stationarityToolkit

    Statistical toolkit to make time-series stationary

    Language:Python0311

  • nakshatra108/Detection-of-Seasonal-Patterns-in-Time-Series-Data

    Used First Difference Method for Stationarity of the Time Series and then Used ARIMA & SARIMA to predict the values and based on the prediction, checked if the series contains Seasonal Patterns in it or not

    Language:Jupyter Notebook0100

  • nihar-phadnis/Time-Series-with-ARIMA

    Time Series forecasting using Seasonal ARIMA. Applied statistical tests like Augmented Dickey–Fuller test to check stationary of series. Checked ACF ,PACF plots. Transformed series to make it stationary

    Language:Jupyter Notebook0100

  • OnurSahin20/Statistical_Analysis

    Code for analyzing statistical features of several precipitation stations

    Language:Jupyter Notebook0100